Prediction of width and depth of laser‐engraved microgrooves: Machine learning versus response surface modelling
Bibliographic record
Abstract
Abstract This study presents a comparative evaluation of two predictive approaches for determining microgroove dimensions in laser machining. The first approach employs response surface methodology (RSM) regression models to predict microgroove width and depth using three input parameters: laser power (10–20 W), scanning rate (50–150 mm/s), and focus distance (6–8 mm). The second approach utilizes data‐driven machine learning (ML) and deep neural network (DNN) models, incorporating five input parameters: laser power, scanning rate, focus distance, laser pass number (1–3), and measurement location (edge and middle). A total of 350 microgrooves were analyzed, and results indicate that the DNN model achieved the highest prediction accuracy, with an R 2 value exceeding 0.94 for depth prediction and a mean absolute error of 10.96 μm on the training data. These findings demonstrate the potential of data‐driven models in improving the precision of laser machining predictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".